Coarse-grained molecular dynamics integrated with convolutional neural network for comparing shapes of temperature sensitive bottlebrushes

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dc.contributor.authorJoshi, Soumil Y.en
dc.contributor.authorSingh, Samrendraen
dc.contributor.authorDeshmukh, Sanket A.en
dc.date.accessioned2022-07-12T12:44:44Zen
dc.date.available2022-07-12T12:44:44Zen
dc.date.issued2022-03-18en
dc.description.abstractQuantification of shape changes in nature-inspired soft material architectures of stimuli-sensitive polymers is critical for controlling their properties but is challenging due to their softness and flexibility. Here, we have computationally designed uniquely shaped bottlebrushes of a thermosensitive polymer, poly(N-isopropylacrylamide) (PNIPAM), by controlling the length of side chains along the backbone. Coarse-grained molecular dynamics simulations of solvated bottlebrushes were performed below and above the lower critical solution temperature of PNIPAM. Conventional analyses (free volume, asphericity, etc.) show that lengths of side chains and their immediate environments dictate the compactness and bending in these architectures. We further developed 100 unique convolutional neural network models that captured molecular-level features and generated a statistically significant quantification of the similarity between different shapes. Thus, our study provides insights into the shapes of complex architectures as well as a general method to analyze them. The shapes presented here may inspire the synthesis of new bottlebrushes.en
dc.description.notesThe authors would like to thank Mr. Dhruv Sharma for productive discussions regarding convolutional neural networks (CNNs) and their application in studying 3D systems like the ones in this work. This work was supported by GlycoMIP, a National Science Foundation Materials Innovation Platform funded through Cooperative Agreement DMR-1933525. The authors would like to acknowledge Advanced Research Computing (ARC) at Virginia Tech for computational resources. This research also used resources of the National Energy Research Scientific Computing Center (NERSC), a scientific computing facility for the Office of Science in the United States Department of Energy, operated under Contract No. DE-AC0205CH11231.en
dc.description.sponsorshipGlycoMIP, a National Science Foundation Materials Innovation Platform [DMR-1933525]; Office of Science in the United States Department of Energy [DE-AC02-05CH11231]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1038/s41524-022-00725-7en
dc.identifier.eissn2057-3960en
dc.identifier.issue1en
dc.identifier.other45en
dc.identifier.urihttp://hdl.handle.net/10919/111215en
dc.identifier.volume8en
dc.language.isoenen
dc.publisherNature Portfolioen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectnanoparticle shapeen
dc.subjectconfigurational entropyen
dc.subjectconformational transitionsen
dc.subjectx-rayen
dc.subjectpolymersen
dc.subjectmodelen
dc.subjectwateren
dc.subjectmacromoleculesen
dc.subjectrecognitionen
dc.subjectsimulationsen
dc.titleCoarse-grained molecular dynamics integrated with convolutional neural network for comparing shapes of temperature sensitive bottlebrushesen
dc.title.serialNpj Computational Materialsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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